Breast Cancer Detection

Using Machine Learning To Predict Diagnosis of a Breast Cancer

1. Identify the problem

Breast cancer is the most common malignancy among women, accounting for nearly 1 in 3 cancers diagnosed among women in the United States, and it is the second leading cause of cancer death among women. Breast Cancer occurs as a results of abnormal growth of cells in the breast tissue, commonly referred to as a Tumor. A tumor does not mean cancer - tumors can be benign (not cancerous), pre-malignant (pre-cancerous), or malignant (cancerous). Tests such as MRI, mammogram, ultrasound and biopsy are commonly used to diagnose breast cancer performed.

1.1 Expected outcome

Given breast cancer results from breast fine needle aspiration (FNA) test (is a quick and simple procedure to perform, which removes some fluid or cells from a breast lesion or cyst (a lump, sore or swelling) with a fine needle similar to a blood sample needle). Since this build a model that can classify a breast cancer tumor using two training classification:

• 1= Malignant (Cancerous) - Present
• 0= Benign (Not Cancerous) -Absent

1.2 Objective

Since the labels in the data are discrete, the predication falls into two categories, (i.e. Malignant or benign). In machine learning this is a classification problem.

Thus, the goal is to classify whether the breast cancer is benign or malignant and predict the recurrence and non-recurrence of malignant cases after a certain period. To achieve this we have used machine learning classification methods to fit a function that can predict the discrete class of new input.

1.3 Identify data sources

The Breast Cancer datasets is available machine learning repository maintained by the University of California, Irvine. The dataset contains 569 samples of malignant and benign tumor cells.

• The first two columns in the dataset store the unique ID numbers of the samples and the corresponding diagnosis (M=malignant, B=benign), respectively.
• The columns 3-32 contain 30 real-value features that have been computed from digitized images of the cell nuclei, which can be used to build a model to predict whether a tumor is benign or malignant.

Attribute Information:

• ID number
• Diagnosis (M = malignant, B = benign)

Ten real-valued features are computed for each cell nucleus:

1. radius (mean of distances from center to points on the perimeter)

• texture (standard deviation of gray-scale values)
• perimeter
• area
• smoothness (local variation in radius lengths)
• compactness (perimeter^2 / area - 1.0)
• concavity (severity of concave portions of the contour)
• concave points (number of concave portions of the contour)
• symmetry
• fractal dimension ("coastline approximation" - 1

Step 1: Import Dependencies

# importing Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score
from sklearn.metrics import classification_report
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_validate, cross_val_score
from sklearn.svm import SVC
from sklearn import metrics
from  sklearn.model_selection import GridSearchCV


%matplotlib inline
sns.set_style('darkgrid')

1. Data Collection.

# load dataset
df = pd.read_csv('data.csv')
df.head()

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	Unnamed: 32
0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	...	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890	NaN
1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	...	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902	NaN
2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	...	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758	NaN
3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	...	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300	NaN
4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	...	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678	NaN

5 rows × 33 columns

df.shape

(569, 33)

df.tail()

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	Unnamed: 32
564	926424	M	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	0.13890	...	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115	NaN
565	926682	M	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	...	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637	NaN
566	926954	M	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	...	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820	NaN
567	927241	M	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	...	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400	NaN
568	92751	B	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	...	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039	NaN

5 rows × 33 columns

2. Exploring Data Analysis

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 33 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   id                       569 non-null    int64  
 1   diagnosis                569 non-null    object 
 2   radius_mean              569 non-null    float64
 3   texture_mean             569 non-null    float64
 4   perimeter_mean           569 non-null    float64
 5   area_mean                569 non-null    float64
 6   smoothness_mean          569 non-null    float64
 7   compactness_mean         569 non-null    float64
 8   concavity_mean           569 non-null    float64
 9   concave points_mean      569 non-null    float64
 10  symmetry_mean            569 non-null    float64
 11  fractal_dimension_mean   569 non-null    float64
 12  radius_se                569 non-null    float64
 13  texture_se               569 non-null    float64
 14  perimeter_se             569 non-null    float64
 15  area_se                  569 non-null    float64
 16  smoothness_se            569 non-null    float64
 17  compactness_se           569 non-null    float64
 18  concavity_se             569 non-null    float64
 19  concave points_se        569 non-null    float64
 20  symmetry_se              569 non-null    float64
 21  fractal_dimension_se     569 non-null    float64
 22  radius_worst             569 non-null    float64
 23  texture_worst            569 non-null    float64
 24  perimeter_worst          569 non-null    float64
 25  area_worst               569 non-null    float64
 26  smoothness_worst         569 non-null    float64
 27  compactness_worst        569 non-null    float64
 28  concavity_worst          569 non-null    float64
 29  concave points_worst     569 non-null    float64
 30  symmetry_worst           569 non-null    float64
 31  fractal_dimension_worst  569 non-null    float64
 32  Unnamed: 32              0 non-null      float64
dtypes: float64(31), int64(1), object(1)
memory usage: 146.8+ KB

df.isna()

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	Unnamed: 32
0	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	True
1	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	True
2	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	True
3	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	True
4	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
564	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	True
565	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	True
566	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	True
567	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	True
568	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	True

569 rows × 33 columns

df.isna().any()

id                         False
diagnosis                  False
radius_mean                False
texture_mean               False
perimeter_mean             False
area_mean                  False
smoothness_mean            False
compactness_mean           False
concavity_mean             False
concave points_mean        False
symmetry_mean              False
fractal_dimension_mean     False
radius_se                  False
texture_se                 False
perimeter_se               False
area_se                    False
smoothness_se              False
compactness_se             False
concavity_se               False
concave points_se          False
symmetry_se                False
fractal_dimension_se       False
radius_worst               False
texture_worst              False
perimeter_worst            False
area_worst                 False
smoothness_worst           False
compactness_worst          False
concavity_worst            False
concave points_worst       False
symmetry_worst             False
fractal_dimension_worst    False
Unnamed: 32                 True
dtype: bool

df.isna().sum() 

id                           0
diagnosis                    0
radius_mean                  0
texture_mean                 0
perimeter_mean               0
area_mean                    0
smoothness_mean              0
compactness_mean             0
concavity_mean               0
concave points_mean          0
symmetry_mean                0
fractal_dimension_mean       0
radius_se                    0
texture_se                   0
perimeter_se                 0
area_se                      0
smoothness_se                0
compactness_se               0
concavity_se                 0
concave points_se            0
symmetry_se                  0
fractal_dimension_se         0
radius_worst                 0
texture_worst                0
perimeter_worst              0
area_worst                   0
smoothness_worst             0
compactness_worst            0
concavity_worst              0
concave points_worst         0
symmetry_worst               0
fractal_dimension_worst      0
Unnamed: 32                569
dtype: int64

df = df.dropna(axis='columns')

Get object features

• Using this method, we can see how many object(categorical) type of feature exists in dataset

df.describe(include="O")

	diagnosis
count	569
unique	2
top	B
freq	357

• As we can see abouve result there are only one single feature is categorical and it's values are B and M

To know how many unique values

df.diagnosis.value_counts()

B    357
M    212
Name: diagnosis, dtype: int64

using value_counts method we can see number of unique values in categorical type of feature.

Identify dependent and independent

df.head(2)

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	842302	M	17.99	10.38	122.8	1001.0	0.11840	0.27760	0.3001	0.14710	...	25.38	17.33	184.6	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	842517	M	20.57	17.77	132.9	1326.0	0.08474	0.07864	0.0869	0.07017	...	24.99	23.41	158.8	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902

2 rows × 32 columns

diagnosis_unique = df.diagnosis.unique()
diagnosis_unique

array(['M', 'B'], dtype=object)

3. Data Visualization.

plt.figure(figsize=(15, 5))

plt.subplot(1, 2, 1)
plt.hist(df['diagnosis'])
plt.title("Counts of Diagnosis")
plt.xlabel("Diagnosis")

plt.subplot(1, 2, 2)
sns.countplot(x='diagnosis', data=df)
plt.title("Counts of Diagnosis")

plt.show()

px.histogram(df, x='diagnosis')

cols = ["diagnosis", "radius_mean", "texture_mean", "perimeter_mean", "area_mean"]

sns.pairplot(df[cols], hue="diagnosis")
plt.show()

size = len(df['texture_mean'])

area = np.pi * (15 * np.random.rand( size ))**2
colors = np.random.rand( size )

plt.xlabel("texture mean")
plt.ylabel("radius mean") 
plt.scatter(df['texture_mean'], df['radius_mean'], s=area, c=colors, alpha=0.5);

Data Filtering

df.head(2)

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	842302	M	17.99	10.38	122.8	1001.0	0.11840	0.27760	0.3001	0.14710	...	25.38	17.33	184.6	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	842517	M	20.57	17.77	132.9	1326.0	0.08474	0.07864	0.0869	0.07017	...	24.99	23.41	158.8	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902

2 rows × 32 columns

labelencoder_Y = LabelEncoder()
df.diagnosis = labelencoder_Y.fit_transform(df.diagnosis)

df.head(2)

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	842302	1	17.99	10.38	122.8	1001.0	0.11840	0.27760	0.3001	0.14710	...	25.38	17.33	184.6	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	842517	1	20.57	17.77	132.9	1326.0	0.08474	0.07864	0.0869	0.07017	...	24.99	23.41	158.8	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902

2 rows × 32 columns

print(df.diagnosis.value_counts())
print("\n", df.diagnosis.value_counts().sum())

0    357
1    212
Name: diagnosis, dtype: int64

 569

Finnaly, We can see in this output categorical values converted into 0 and 1.

Find the correlation between other features, mean features only

cols = ['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',
       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',
       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean']
print(len(cols))
df[cols].corr()

11

	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean
diagnosis	1.000000	0.730029	0.415185	0.742636	0.708984	0.358560	0.596534	0.696360	0.776614	0.330499	-0.012838
radius_mean	0.730029	1.000000	0.323782	0.997855	0.987357	0.170581	0.506124	0.676764	0.822529	0.147741	-0.311631
texture_mean	0.415185	0.323782	1.000000	0.329533	0.321086	-0.023389	0.236702	0.302418	0.293464	0.071401	-0.076437
perimeter_mean	0.742636	0.997855	0.329533	1.000000	0.986507	0.207278	0.556936	0.716136	0.850977	0.183027	-0.261477
area_mean	0.708984	0.987357	0.321086	0.986507	1.000000	0.177028	0.498502	0.685983	0.823269	0.151293	-0.283110
smoothness_mean	0.358560	0.170581	-0.023389	0.207278	0.177028	1.000000	0.659123	0.521984	0.553695	0.557775	0.584792
compactness_mean	0.596534	0.506124	0.236702	0.556936	0.498502	0.659123	1.000000	0.883121	0.831135	0.602641	0.565369
concavity_mean	0.696360	0.676764	0.302418	0.716136	0.685983	0.521984	0.883121	1.000000	0.921391	0.500667	0.336783
concave points_mean	0.776614	0.822529	0.293464	0.850977	0.823269	0.553695	0.831135	0.921391	1.000000	0.462497	0.166917
symmetry_mean	0.330499	0.147741	0.071401	0.183027	0.151293	0.557775	0.602641	0.500667	0.462497	1.000000	0.479921
fractal_dimension_mean	-0.012838	-0.311631	-0.076437	-0.261477	-0.283110	0.584792	0.565369	0.336783	0.166917	0.479921	1.000000

plt.figure(figsize=(12, 9))

plt.title("Correlation Graph")

cmap = sns.diverging_palette( 1000, 120, as_cmap=True)
sns.heatmap(df[cols].corr(), annot=True, fmt='.1%',  linewidths=.05, cmap=cmap);

Using, Plotly Pacage we can show it in interactive graphs like this,

plt.figure(figsize=(15, 10))


fig = px.imshow(df[cols].corr());
fig.show()

<Figure size 1500x1000 with 0 Axes>

Model Implementation¶

Train Test Splitting

Feature Selection¶

Select feature for predictions

df.columns

Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',
       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',
       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
       'fractal_dimension_se', 'radius_worst', 'texture_worst',
       'perimeter_worst', 'area_worst', 'smoothness_worst',
       'compactness_worst', 'concavity_worst', 'concave points_worst',
       'symmetry_worst', 'fractal_dimension_worst'],
      dtype='object')

• take the dependent and independent feature for prediction

prediction_feature = [ "radius_mean",  'perimeter_mean', 'area_mean', 'symmetry_mean', 'compactness_mean', 'concave points_mean']

targeted_feature = 'diagnosis'

len(prediction_feature)

6

X = df[prediction_feature]
X

	radius_mean	perimeter_mean	area_mean	symmetry_mean	compactness_mean	concave points_mean
0	17.99	122.80	1001.0	0.2419	0.27760	0.14710
1	20.57	132.90	1326.0	0.1812	0.07864	0.07017
2	19.69	130.00	1203.0	0.2069	0.15990	0.12790
3	11.42	77.58	386.1	0.2597	0.28390	0.10520
4	20.29	135.10	1297.0	0.1809	0.13280	0.10430
...	...	...	...	...	...	...
564	21.56	142.00	1479.0	0.1726	0.11590	0.13890
565	20.13	131.20	1261.0	0.1752	0.10340	0.09791
566	16.60	108.30	858.1	0.1590	0.10230	0.05302
567	20.60	140.10	1265.0	0.2397	0.27700	0.15200
568	7.76	47.92	181.0	0.1587	0.04362	0.00000

569 rows × 6 columns

y = df.diagnosis
y

0      1
1      1
2      1
3      1
4      1
      ..
564    1
565    1
566    1
567    1
568    0
Name: diagnosis, Length: 569, dtype: int32

• Splite the dataset into TrainingSet and TestingSet by 33% and set the 15 fixed records

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=15)

print(X_train)
# print(X_test)

     radius_mean  perimeter_mean  area_mean  symmetry_mean  compactness_mean  \
274        17.93          115.20      998.9         0.1538           0.07027   
189        12.30           78.83      463.7         0.1667           0.07253   
158        12.06           76.84      448.6         0.1590           0.05241   
257        15.32          103.20      713.3         0.2398           0.22840   
486        14.64           94.21      666.0         0.1409           0.06698   
..           ...             ...        ...            ...               ...   
85         18.46          121.10     1075.0         0.2132           0.10530   
199        14.45           94.49      642.7         0.1950           0.12060   
156        17.68          117.40      963.7         0.1971           0.16650   
384        13.28           85.79      541.8         0.1617           0.08575   
456        11.63           74.87      415.1         0.1799           0.08574   

     concave points_mean  
274              0.04744  
189              0.01654  
158              0.01963  
257              0.12420  
486              0.02791  
..                   ...  
85               0.08795  
199              0.05980  
156              0.10540  
384              0.02864  
456              0.02017  

[381 rows x 6 columns]

Perform Feature Standerd Scalling

Standardize features by removing the mean and scaling to unit variance

The standard score of a sample x is calculated as:

• z = (x - u) / s

# Scale the data to keep all the values in the same magnitude of 0 -1 

sc = StandardScaler()

X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)

ML Model Selecting and Model PredPrediction¶

Model Building¶

Arguments

1. model => ML Model Object
2. Feature Training Set data
3. Feature Testing Set data
4. Targetd Training Set data
5. Targetd Testing Set data

def model_building(model, X_train, X_test, y_train, y_test):
    """
    
    Model Fitting, Prediction And Other stuff
    return ('score', 'accuracy_score', 'predictions' )
    """
    
    model.fit(X_train, y_train)
    score = model.score(X_train, y_train)
    predictions = model.predict(X_test)
    accuracy = accuracy_score(predictions, y_test)
    
    return (score, accuracy, predictions) 

Let's make a dictionary for multiple models for bulk predictions

models_list = {
    "LogisticRegression" :  LogisticRegression(),
    "RandomForestClassifier" :  RandomForestClassifier(n_estimators=10, criterion='entropy', random_state=5),
    "DecisionTreeClassifier" :  DecisionTreeClassifier(criterion='entropy', random_state=0),
    "SVC" :  SVC(),
}

print(list(models_list.keys()))
print(list(models_list.values()))

['LogisticRegression', 'RandomForestClassifier', 'DecisionTreeClassifier', 'SVC']
[LogisticRegression(), RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=5), DecisionTreeClassifier(criterion='entropy', random_state=0), SVC()]

Model Implementing

Now, Train the model one by one and show the classification report of perticular models wise.

Let's Define the function for confision metric Graphs

def cm_metrix_graph(cm):
    
    sns.heatmap(cm,annot=True,fmt="d")
    plt.show()
        

df_prediction = []
confusion_matrixs = []
df_prediction_cols = [ 'model_name', 'score', 'accuracy_score' , "accuracy_percentage"]

for name, model in zip(list(models_list.keys()), list(models_list.values())):
    
    (score, accuracy, predictions) = model_building(model, X_train, X_test, y_train, y_test )
    
    print("\n\nClassification Report of '"+ str(name), "'\n")
    
    print(classification_report(y_test, predictions))

    df_prediction.append([name, score, accuracy, "{0:.2%}".format(accuracy)])
    
    # For Showing Metrics
    confusion_matrixs.append(confusion_matrix(y_test, predictions))
    
        
df_pred = pd.DataFrame(df_prediction, columns=df_prediction_cols)

Classification Report of 'LogisticRegression '

              precision    recall  f1-score   support

           0       0.90      0.96      0.93       115
           1       0.92      0.84      0.88        73

    accuracy                           0.91       188
   macro avg       0.91      0.90      0.90       188
weighted avg       0.91      0.91      0.91       188



Classification Report of 'RandomForestClassifier '

              precision    recall  f1-score   support

           0       0.92      0.96      0.94       115
           1       0.93      0.88      0.90        73

    accuracy                           0.93       188
   macro avg       0.93      0.92      0.92       188
weighted avg       0.93      0.93      0.93       188



Classification Report of 'DecisionTreeClassifier '

              precision    recall  f1-score   support

           0       0.90      0.96      0.93       115
           1       0.92      0.84      0.88        73

    accuracy                           0.91       188
   macro avg       0.91      0.90      0.90       188
weighted avg       0.91      0.91      0.91       188



Classification Report of 'SVC '

              precision    recall  f1-score   support

           0       0.90      0.97      0.93       115
           1       0.94      0.84      0.88        73

    accuracy                           0.91       188
   macro avg       0.92      0.90      0.91       188
weighted avg       0.92      0.91      0.91       188

print(len(confusion_matrixs))

4

# Assuming confusion_matrixs is a list of confusion matrices

plt.figure(figsize=(15, 5))

for index, cm in enumerate(confusion_matrixs):
    plt.subplot(1, len(confusion_matrixs), index + 1)  # Create a subplot
    cm_metrix_graph(cm)  # Call the Confusion Metrics Graph for the current confusion matrix
    plt.title(f'Confusion Matrix {index + 1}')  # Set the title for the subplot

plt.tight_layout()
plt.show()

C:\Users\maashree\AppData\Local\Temp\ipykernel_16308\830019773.py:6: MatplotlibDeprecationWarning:

Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.

df_pred

	model_name	score	accuracy_score	accuracy_percentage
0	LogisticRegression	0.916010	0.909574	90.96%
1	RandomForestClassifier	0.992126	0.925532	92.55%
2	DecisionTreeClassifier	1.000000	0.909574	90.96%
3	SVC	0.923885	0.914894	91.49%

• print the hightest accuracy score using sort values

df_pred.sort_values('score', ascending=False)
df_pred.sort_values('accuracy_score', ascending=False)

	model_name	score	accuracy_score	accuracy_percentage
1	RandomForestClassifier	0.992126	0.925532	92.55%
3	SVC	0.923885	0.914894	91.49%
0	LogisticRegression	0.916010	0.909574	90.96%
2	DecisionTreeClassifier	1.000000	0.909574	90.96%

Conclusion

• In this kernal, We had seen the data clearning and EDA using pandas methods and show some visual graphs to know the behaviour of this dataset and finnaly we train some model for it and calculate the prediction and it's acciracy scores and hyper tunning